9 papers
CHIME: Credit-Aware Hierarchical Memory Evolution for Long-Horizon Agentic Planning
Yongshi Ye, Tian Lan, Feihu Jiang +7
Planning is a central capability that enables agents to decompose complex long-horizon tasks into manageable steps. Test-time search and training-based methods improve planning but…
Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning
Muyang Ye, Tian Lan, Feihu Jiang +10
Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-evolution in expert domains.…
Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
Bin Zhu, Qianghuai Jia, Tian Lan +6
Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world…
UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
Yongshi Ye, Hui Jiang, Feihu Jiang +7
Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updati…
Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
Tian Lan, Felix Henry, Bin Zhu +7
Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states, including planning procedure a…
DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking
Tian Lan, Bin Zhu, Qianghuai Jia +6
Current search agents fundamentally lack the ability to simultaneously perform \textit{deep} reasoning over multi-hop retrieval and \textit{wide}-scale information collection-a cri…
HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application
Yiqian Yang, Tian Lan, Qianghuai Jia +6
Effective deep search agents must not only access open-domain and domain-specific knowledge but also apply complex rules-such as legal clauses, medical manuals and tariff rules. Th…
CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation
Ningxin Gui, Qianghuai Jia, Feijun Jiang +3
We introduce CRPE (Code Reasoning Process Enhancer), an innovative three-stage framework for data synthesis and model training that advances the development of sophisticated code r…
ADC: Enhancing Function Calling Via Adversarial Datasets and Code Line-Level Feedback
Wei Zhang, Yi Zhang, Li Zhu +5
Large Language Models (LLMs) have made significant strides in Natural Language Processing and coding, yet they struggle with robustness and accuracy in complex function calls. To t…